Executive Summary

A longstanding challenge of financial planning has been the fact that its value is usually defined in intangible terms (e.g., “bringing peace of mind”) or at least over time horizons too long to effectively evaluate (e.g., “helping people achieve their long-term goals”). Yet arguably, the value of financial planning could be better quantified, by trying to measure how much economically better off clients are by engaging in financial planning strategies than what they would have otherwise done.

And in a recent research paper entitled “Alpha, Beta, and Now… Gamma” David Blanchett and Paul Kaplan of Morningstar have attempted to do exactly this – evaluating how the financial outcomes of retirees are improved by engaging in five financial planning strategies, from more effective asset allocation to dynamic withdrawal rate spending approaches to proper asset location decisions.

Quantifying the difference between the baseline and financial-planning-optimal strategies as “Gamma”, Blanchett and Kaplan find that good financial planning decisions increase retirement income by 29%, which is the equivalent of generating 1.82%/year of higher returns. Although there are some important caveats to the research, the new Morningstar paper may open the door to a wave of new research attempting to measure the “Gamma” of good financial planning.

Defining Gamma

Both investors and advisors are familiar with the concept of alpha and beta, where beta measures exposure to systematic risk (and the associated expected return), while alpha measures the excess (or negative) returns created beyond those simply due to market beta exposure.

According to the “Alpha, Beta, and Now… Gamma” Morningstar paper by David Blanchett and Paul Kaplan, Gamma – which follows alpha and beta in the Greek alphabet – is meant to measure “the additional expected retirement income achieved by an individual investor from making more intelligent financial planning decisions.” Thus, in principle Gamma is similar to alpha, in that it measures a form of excess return and value brought to the table. The difference, however, is that Gamma is a result of financial planning decisions, rather than investment decisions.

And notably, because Gamma represents the execution of more optimal financial planning strategies, it doesn’t necessarily have to average out to zero in the aggregate the way that alpha does. For instance, if every financial planning client generates retirement income in a more tax-efficient manner by properly locating investments in the correct account, then they can all simultaneously enjoy positive Gamma through optimal financial planning decisions.

Measuring Gamma

Although financial planning spans a tremendously broad range of potential decisions, in this paper, Blanchett and Kaplan start out by measuring Gamma for five specific retirement-based planning issues: determining asset allocation based on total wealth; applying a dynamic safe withdrawal rate strategy; incorporating guaranteed retirement income products (e.g., annuities); making tax-efficient allocation (i.e., asset location) decisions; and optimizing the portfolio by treating client cash flow needs as liabilities and matching investments and their risks appropriate to manage (and hedge) those liability needs.

Of course, in order to measure the excess/improved results attributable to such financial planning decisions, it’s also necessary to choose a baseline – what was the client assumed to do in the absence of financial planning guidance and wisdom. For instance, in terms of asset allocation, the client is assumed to simply allocate investment assets to be 20% in equities (the average for investors aged 65 to 95 in the 2010 Survey of Consumer Finances), compared to a financial-planning-efficient method that aims to be 45% in equities (to match the market portfolio of all public and non-publically-traded securities) and is adjusted for the net present value of fixed income streams (i.e., the NPV of Social Security is treated like a bond on the client’s balance sheet to determine asset allocation). Similarly, the dynamic withdrawal rate strategy – based on the “Mortality Updating Failure Percentage” approach outlined in “Optimal Withdrawal Strategy for Retirement Income Portfolios” by Blanchett, Kowara, and Chen (2012), is compared to a simply 4% safe withdrawal rate strategy that is not otherwise adjusted after the starting year (except for inflation adjustments).

So what’s the value of these various strategies? As noted earlier, the research concluded that the total benefit was approximately 29% higher retirement income, which was equivalent to generating 1.82%/year of excess return. The breakdown of the relative benefit by engaging in each of the strategies, from Table 5 of the paper, is shown below:

Gamma Strategy

Additional Income

Gamma

Total Wealth Asset Allocation

6.1%

0.38%

Annuity Allocation

3.8%

0.24%

Dynamic Withdrawal Strategy

8.5%

0.54%

Liability Relative Optimization

2.2%

0.14%

Asset Location/Withdrawal Sourcing

8.2%

0.52%

Total

28.8%

1.82%

Benefits and Caveats of Gamma

In a world where much of the value of financial planning is so difficult to measure, “Gamma research” represents a new avenue to explore to substantiate the prices that financial planners charge. The aforementioned Gamma results for retirees alone suggest that, at 1.82% of Gamma, planners charging “just” 1%/year for managing retirement assets and providing ongoing financial planning advice have a huge net positive effect on generating retirement income, in addition to all the more intangible benefits of financial planning.

Of course, many of the factors discussed here are not necessarily relevant for all clients – for instance, the use of annuities and also dynamic withdrawal strategies to generate retirement income is a moot point for clients that haven’t retired yet – and some financial planning strategies that are relevant for other client types and scenarios weren’t measured at all. Thus, the avenue is open for other Gamma research studies to emerge on a wider range of financial planning decisions and advice.

Notwithstanding all of this, there are some important caveats to the Morningstar Gamma research as well. The biggest, by far, is that many of the results are heavily impacted by the baseline that is chosen, which is arguably quite subjective. For instance, if the default asset allocation was assumed to be 50/50 in equities and fixed income, instead of only 20/80, the benefits of total wealth allocation may be diminished; while the research does justify the 20% equity exposure baseline from the Survey of Consumer Finances, it’s not clear whether those measured by the survey (and the associated results) are really representative of the people who typically engage financial planners. Similarly, the benefits of using an annuity may depend not only on the client’s relative health and life expectancy, but also indirectly on the withdrawal rate methodology – a lower starting withdrawal rate may diminish the value of the annuity (as it’s more likely money will be left over anyway), although changing the baseline spending amount can also impact the value of the dynamic withdrawal strategy. To say the least, subsequent research by Blanchett and/or others may wish to explore how sensitive the Gamma results of some strategies are to the underlying assumptions used to measure them.

Another caveat of the research is that in order to measure the Gamma benefit, it’s necessary to determine what is and is not a “better” outcome. To Blanchett and Kaplan’s credit, they don’t simply determine the results based on return and wealth alone. They actually measure financial planning results using a utility function, and analyze the results on a Monte Carlo basis, accounting both for volatility and the fact that clients will weigh downturns and spending cuts more heavily than incrementally greater amounts of excess wealth. Nonetheless, the utility functions and equations used in the research themselves rely on several assumptions, including client risk tolerance/preferences, willingness to consume now versus in the future, and more; although the research found the results were relatively unaffected by varying values for these preferences, there is still some risk that “more effective” measures of client preferences in the future may change the Gamma results (at least for some financial planning strategies).

Notwithstanding these concerns, I suspect that Blanchett, Kaplan, and Morningstar overall may have opened a new line of “Gamma research” that attempts to measure the quantitative and economic value of financial planning strategies, relative to some alternative baseline that clients would be assumed to engage in without the presence of a planner. To some extent, this has already been present in the investment realm, as many planners have long suggested that one of their primary benefits is to keep clients invested and avoid the so-called “Behavior Gap” of do-it-yourself-investor underperformance as commonly measured by DALBAR (although recent analysis suggests this benefit may be overstated). But this Gamma research shows how the analysis can extend beyond just the behavior gap investment research, also looking at strategies like effective asset location and tax planning and proper use of investment and insurance products as well.

At a minimum, the Gamma research opens the door to efforts to move discussions about the value of financial planning away from being based on purely intangible and emotional benefits, and into a world where financial planning simply makes good mathematical financial sense. And 1.82%/year is a pretty good start.